"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

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modification, are permitted provided that the following conditions are met:

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  and/or other materials provided with the distribution.

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  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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"""
# Copyright 2021 Sea Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Adapted for VOLO
'''
- load_pretrained_weights: load pretrained paramters to model in transfer learning
- resize_pos_embed: resize position embedding
- get_mean_and_std: calculate the mean and std value of dataset.
'''
import torch
import math

import logging
import os
from collections import OrderedDict
import torch.nn.functional as F

_logger = logging.getLogger(__name__)


def resize_pos_embed(posemb, posemb_new):
    '''
    resize position embedding with class token
    example: 224:(14x14+1)-> 384: (24x24+1)
    return: new position embedding
    '''
    ntok_new = posemb_new.shape[1]

    posemb_tok, posemb_grid = posemb[:, :1], posemb[0,1:]  # posemb_tok is for cls token, posemb_grid for the following tokens
    ntok_new -= 1
    gs_old = int(math.sqrt(len(posemb_grid)))  # 14
    gs_new = int(math.sqrt(ntok_new))  # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(
        0, 3, 1, 2)  # [1, 196, dim]->[1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(
        posemb_grid, size=(gs_new, gs_new),
        mode='bicubic')  # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(
        1, gs_new * gs_new, -1)  # [1, dim, 24, 24] -> [1, 24*24, dim]
    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)  # [1, 24*24+1, dim]
    return posemb


def resize_pos_embed_without_cls(posemb, posemb_new):
    '''
    resize position embedding without class token
    example: 224:(14x14)-> 384: (24x24)
    return new position embedding
    '''
    ntok_new = posemb_new.shape[1]
    posemb_grid = posemb[0]
    gs_old = int(math.sqrt(len(posemb_grid)))  # 14
    gs_new = int(math.sqrt(ntok_new))  # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(
        0, 3, 1, 2)  # [1, 196, dim]->[1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(
        posemb_grid, size=(gs_new, gs_new),
        mode='bicubic')  # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(
        1, gs_new * gs_new, -1)  # [1, dim, 24, 24] -> [1, 24*24, dim]
    return posemb_grid


def resize_pos_embed_4d(posemb, posemb_new):
    '''return new position embedding'''
    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
    gs_old = posemb.shape[1]  # 14
    gs_new = posemb_new.shape[1]  # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb
    posemb_grid = posemb_grid.permute(0, 3, 1,
                                      2)  # [1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bicubic')  # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1)  # [1, dim, 24, 24]->[1, 24, 24, dim]
    return posemb_grid

def load_state_dict(checkpoint_path, model, use_ema=False, num_classes=1000):
    # load state_dict
    if checkpoint_path and os.path.isfile(checkpoint_path):
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        state_dict_key = 'state_dict'
        if isinstance(checkpoint, dict):
            if use_ema and 'state_dict_ema' in checkpoint:
                state_dict_key = 'state_dict_ema'
        if state_dict_key and state_dict_key in checkpoint:
            new_state_dict = OrderedDict()
            for k, v in checkpoint[state_dict_key].items():
                # strip `module.` prefix
                name = k[7:] if k.startswith('module') else k
                new_state_dict[name] = v
            state_dict = new_state_dict
        else:
            state_dict = checkpoint
        _logger.info("Loaded {} from checkpoint '{}'".format(
            state_dict_key, checkpoint_path))
        if num_classes != 1000:
            # completely discard fully connected for all other differences between pretrained and created model
            del state_dict['head' + '.weight']
            del state_dict['head' + '.bias']
            old_aux_head_weight = state_dict.pop('aux_head.weight', None)
            old_aux_head_bias = state_dict.pop('aux_head.bias', None)

        old_posemb = state_dict['pos_embed']
        if model.pos_embed.shape != old_posemb.shape:  # need resize the position embedding by interpolate
            if len(old_posemb.shape) == 3:
                if int(math.sqrt(
                        old_posemb.shape[1]))**2 == old_posemb.shape[1]:
                    new_posemb = resize_pos_embed_without_cls(
                        old_posemb, model.pos_embed)
                else:
                    new_posemb = resize_pos_embed(old_posemb, model.pos_embed)
            elif len(old_posemb.shape) == 4:
                new_posemb = resize_pos_embed_4d(old_posemb, model.pos_embed)
            state_dict['pos_embed'] = new_posemb

        return state_dict
    else:
        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
        raise FileNotFoundError()


def load_pretrained_weights(model,
                            checkpoint_path,
                            use_ema=False,
                            strict=True,
                            num_classes=1000):
    '''load pretrained weight for VOLO models'''
    state_dict = load_state_dict(checkpoint_path, model, use_ema, num_classes)
    model.load_state_dict(state_dict, strict=strict)


def get_mean_and_std(dataset):
    '''Compute the mean and std value of dataset.'''
    dataloader = torch.utils.data.DataLoader(dataset,
                                             batch_size=1,
                                             shuffle=True,
                                             num_workers=2)
    mean = torch.zeros(3)
    std = torch.zeros(3)
    print('==> Computing mean and std..')
    for inputs, targets in dataloader:
        for i in range(3):
            mean[i] += inputs[:, i, :, :].mean()
            std[i] += inputs[:, i, :, :].std()
    mean.div_(len(dataset))
    std.div_(len(dataset))
    return mean, std
